Economy Intel MCP

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It discloses a meaningful behavioral trait: only the latest available value per country is used, not a time series. However, it does not describe what happens with missing data, the return structure, or other edge-case behavior, leaving some gaps.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two short sentences with no filler. The core behavior is front-loaded, and the second sentence adds a directly useful use case rather than redundant detail.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple two-parameter tool, the description is largely complete: it explains the purpose, the input focus, and the type of comparison. It does not describe the exact output format, but the phrase 'compare across countries' sufficiently implies a per-country result list or mapping.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema already provides 100% parameter coverage with detailed descriptions for countries and indicator. The description adds little parameter-specific meaning beyond reinforcing that a single indicator is compared across countries, so the baseline of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb and resource: 'Compare one indicator across several countries.' It also adds the important qualifier 'latest available value each,' making the tool's scope precise and distinguishing it from single-country or time-series siblings like country_indicator and country_profile.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The phrase 'Good for ranking or benchmarking economies' gives a clear use case and context for when this tool is appropriate. However, it does not explicitly state when not to use it or name alternatives such as country_indicator or us_series.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are present, so the description carries the transparency burden. It discloses that the tool returns recent-years data, accepts ISO/World Bank country codes, and supports raw World Bank indicator codes. However, it does not describe the output shape, units, missing-data behavior, or data freshness, which is a notable gap for a data lookup tool with no output schema.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is compact and front-loaded, stating the core purpose first and then listing supported indicators and input variations. The indicator enumeration is long but useful for quick scanning, and there is no filler or redundant explanation.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema and no annotations, the description should clarify what a successful call returns. It indicates recent-years data and enumerates inputs, but it does not state the return format, units, or potential quirks like missing years or unavailable indicators, and it does not mention the years default. This leaves a moderate completeness gap.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the baseline is 3. The description mostly restates the indicator list and country-format guidance already present in the schema; the extra mention of 'World Bank country code' adds only marginal clarity beyond 'ISO code or name.' It provides no additional semantics for the years parameter.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a clear verb-resource pair: 'Get a macroeconomic indicator for a country over recent years (World Bank)' and enumerates the supported indicators. It is easy to tell this is a single-country data lookup, but it does not explicitly distinguish itself from siblings like compare_countries or us_series.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies when to use it: anytime you need a single country's macroeconomic indicator over recent years. However, it provides no explicit guidance about alternatives, such as using compare_countries for cross-country comparisons or list_indicators to discover all available indicator codes, so usage guidance remains only implicit.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description must carry the behavioral burden. It communicates that the tool returns a current snapshot rather than historical series, but it does not disclose output format, units, data source, or behavior on invalid country codes. The 'Get' wording implies read-only, but minimal detail is added.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, focused sentence that front-loads the tool's purpose and immediately lists the indicators returned. Every phrase contributes to understanding, with no redundant or filler content.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple one-parameter tool with no output schema, the description is mostly complete: it names the country input, the result is a snapshot, and the exact indicators are enumerated. It could be more complete by noting what to expect if data is unavailable or by guiding toward siblings, but the core invocation is well covered.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already fully documents the country parameter. The description adds the note 'Country accepts an ISO code,' which is a useful shorthand but less detailed than the schema's own 'ISO code or name' explanation and provides no additional semantic value.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb ('Get a snapshot') and clearly identifies the resource (a country's key macro indicators) with an explicit list of indicators. This differentiates it from siblings like list_indicators and us_series, though it does not explicitly contrast with country_indicator.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies when to use the tool ('snapshot', 'latest available values') but gives no explicit guidance about when not to use it or which sibling to prefer instead, such as country_indicator for a single indicator series or compare_countries for cross-country comparisons.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

There are no annotations, so the description carries full responsibility. It discloses that the tool returns a list with descriptions and is non-destructive by nature, but it does not mention pagination, response size, or whether any filtering exists. This is acceptable for a simple listing tool but leaves some behavior unstated.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two tight sentences with no filler: the first states the action and scope, the second gives a direct usage directive. Every sentence earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a zero-parameter discovery tool, the description is complete enough: it names the data sources, says descriptions are included, and tells the agent to call it before querying. No significant missing information prevents correct invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool has zero parameters, so the schema fully covers the input surface. The description adds useful context about what the returned list contains, which serves the discovery purpose without needing parameter-level detail.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb ('List') and resource ('indicators this server supports'), and explicitly scopes the content to World Bank + US BLS. It is clearly distinct from the sibling query tools because it is the discovery entry point.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

It explicitly instructs the agent to call this to discover what can be queried, which establishes when to use it before querying specific indicators. It does not explicitly name alternatives or state when not to use it, but the discovery-vs-query distinction is implied strongly enough.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden and does so well: it discloses the Datakoot caching/warm-keep behavior, that BLS rate-limits by client IP shared across Datakoot's edge, and crucially how to interpret the upstream-limit error (refusal, not missing series). This is exactly the operational context an agent needs to avoid misdiagnosing failures.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Three sentences, front-loaded with the purpose and the key list before the operational caveat. It is efficient, though the series enumeration is duplicated from the schema, which is mild redundancy rather than waste.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

There is no output schema, yet the description says nothing about the return shape: frequency, units, observation count, or freshness of the series. Input selection and error handling are fully covered, but an agent cannot anticipate what data structure comes back, leaving a real gap for a time-series tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the single 'series' parameter is already fully documented. The description repeats the accepted keys and adds only the rate-limit caveat, which the schema also mentions, so it does not meaningfully extend the schema's semantics. Baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb ('Get') plus resource ('a key US economic time series') and enumerates the exact series keys, which immediately separates it from country-level siblings like country_indicator and country_profile. An agent knows from the first sentence that this is US-specific series retrieval, not country comparison.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives clear context for which keys exist and when to prefer the cached keys over a raw BLS ID, including the explicit caveat that uncached series may fail. It stops short of naming a sibling alternative or stating when to use country_indicator instead, so it is clear but not fully routing.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.